Robotic operations control system for a blended workforce

ABSTRACT

A robotic operations control system enables seamless distribution of tasks among a blended workforce that includes human employees and robot assistants. The robot assistants include an intelligent billing assistant (IBA), an intelligent collections assistant (iNCA), a dispute resolution assistant (DRA) and a cash processor. Each of the assistants includes a workflow manager that receives work items to be processed by the robot assistant based on the priority levels assigned to the work items. The IBA includes a dispute prediction model that predicts the likelihood of dispute for a work item and automatically generates a customized checklist. The iNCA includes an AI prioritizing assistant that identifies priorities for work items to be processed. The DRA includes an AI assisted investigator that expedites investigations of disputes by proactively gathering information for a human employee to review and mark the dispute as valid, invalid or for query.

BACKGROUND

The evolution of artificial intelligence (AI) technologies and relatedprogramming tools is enabling machines to work alongside and collaboratewith human employees. Many organizations are taking significant stridesin this direction by adopting cognitive and AI technologies with theirprocesses. Machines and employees possess different complementary skillsin that machines are good in terms of precision and consistency whileemployees tend to be better at tasks that demand creativity, contextualunderstanding and complex communications. Therefore, moving a battery ofrepetitive, tedious tasks to be handled by the machines may have atwo-fold advantage for a workforce. Not only can the organizationleverage the resulting machine learning to improve the efficiency of thetasks but the employees are also freed up to pursue more challengingtasks thereby raising their skill levels. Many technological challengesexist in developing interfaces that enable the various participants ofthe blended workforce to seamlessly work together towards improvedefficiency.

BRIEF DESCRIPTION OF DRAWINGS

Features of the present disclosure are illustrated by way of examplesshown in the following figures. In the following figures, like numeralsindicate like elements, in which:

FIG. 1 shows a robotic operations control system that enables operationswithin a blended workforce in accordance with an example.

FIG. 2 is a block diagram of an AI assisted billing mailbox manager inaccordance with one example.

FIG. 3 is a block diagram of a workflow manager in accordance with anexample disclosed herein.

FIG. 4 shows a block diagram of a work priority assistant in accordancewith an example.

FIG. 5 shows a block diagram of a disputes prediction model inaccordance with an example.

FIG. 6 is a flowchart that details a method of processing an email by anAI assisted billing mailbox manager in accordance with an example.

FIG. 7 is a flowchart wherein a disputes prediction model is employed togenerate a checklist that enables reducing disputes in accordance withan example.

FIGS. 8A and 8B are flow diagrams that illustrate how a blendedworkforce manages an example task

FIG. 9 shows a user interface which enables assignment of work items toemployees in a DRA in accordance with examples disclosed herein.

FIG. 10 shows the various client ids and billing matter categories whichwere significantly disputed.

FIG. 11 shows a checklist that is generated in accordance with examplesdescribed herein.

FIG. 12 shows a user interface or a real-time dashboard of an iNCA inaccordance with an example.

FIG. 13 shows a resubmission screen in accordance with an example.

FIG. 14 shows a screen to raise a dispute in accordance with an example.

FIG. 15 shows a dashboard of the DRA that in accordance with an example.

FIG. 16 shows an email that was automatically generated for acommunication regarding a dispute in accordance with an example.

FIG. 17 shows a dispute validation screen in accordance with an example.

FIG. 18 is a block diagram that illustrates an example of a computersystem for implementing the robotic operations control system.

DETAILED DESCRIPTION

For simplicity and illustrative purposes, the present disclosure isdescribed by referring mainly to examples thereof. In the followingdescription, numerous specific details are set forth in order to providea thorough understanding of the present disclosure. It will be readilyapparent however that the present disclosure may be practiced withoutlimitation to these specific details. In other instances, some methodsand structures have not been described in detail so as not tounnecessarily obscure the present disclosure. Throughout the presentdisclosure, the terms “a” and “an” are intended to denote at least oneof a particular element. As used herein, the term “includes” meansincludes but not limited to, the term “including” means including butnot limited to. The term “based on” means based at least in part on.

According to one or more examples described herein, a roboticsoperations control system that enables a blended workforce includinghuman employees and robot assistants to efficiently carry out tasks isdisclosed. The robot assistants can include programming instructions orcode that is executed by one or more processors to automatically selectand carry out certain tasks within the work flow that the robotassistants may be programmed to handle. While the description is givenbelow with respect to a robotic operations control system for aparticular department, it can be appreciated that this is forillustration purposes only and that the examples involved in selectingwork items, prioritizing the work items and making predictions asdescribed herein can be equally applicable to other departments whererobot assistants may be employed to work alongside human employees.

The robotics operations control system includes various robot assistantssuch as an intelligent billing assistant (IBA), an intelligentcollections assistant (iNCA), a disputes resolution assistant (DRA) anda cash processor. Each of the assistants enables speeding up processeswithin a certain part of the workflow for billing and collectionsprocesses. Each of the assistants includes a respective workflow managerthat accesses one or more lists of work items assigned thereto andprioritizes the work items based on the information regarding the workitems that is included in current attributes data and historicattributes data of the work items. A work item may pertain to one of abilling matter, a collections matter or a dispute and the currentattributes data includes attribute values of the work item. The historicattributes data includes prior values of the attributes included in thework item wherein the prior values are obtained from similar work itemsthat were previously processed by the robot assistants. A centralrepository stores the current attributes data and the historicattributes data in addition to performance statistics of the variousparticipants of the robotics operations control system. The variousparticipants for whom performance statistics are gathered may includethe human employees and the robot assistants described herein.

The work items for the various assistants may be provided from differentsources such as electronic mailboxes or pending work lists and the like.The workflow managers in each of the various assistants process the workitems in accordance with respective priorities assigned thereto. Forexample, an electronic billing mailbox that receives emails related tobilling matters from within the domain of the robotics operationscontrol system feeds the workflow of the IBA. The workflow manager ofthe IBA accesses the work items designated to the IBA while an AI basedbilling mailbox manager processes the work items in accordance with thevarious request types that the IBA is configured to handle. A disputesprediction model is included in the IBA which employs dispute predictionanalytics to identify those work items associated with billing mattersthat have greater likelihood of being disputed. When a billing matterwith a higher likelihood of dispute is identified, a checklist may begenerated in order to reduce the probability of occurrence of thedispute. A real-time dashboard also included in the IBA, providesvarious user interfaces to access the functionality associated withdifferent elements of the IBA while monitoring the progress of thevarious billing matters.

The iNCA and the DRA share an electronic collections mailbox from whichwork items are forwarded to be processed by the iNCA and by the DRA. Thecollections mailbox not only receives internal email related tocollections matters and disputes from the domain of the roboticsoperation control system but may also receive emails from externaldomains. A workflow analyzer included in the workflow manager analyzesthe emails to identify the emails that are to be processed by the iNCAand the emails that are to be processed by the DRA. An AI prioritizingassistant is also included in the iNCA for identifying priorities forwork items to be processed by the iNCA using intelligent collectionanalytics as will be detailed further infra.

Certain work items such as requests for invoice copies and the like arehandled automatically by the iNCA without human intervention. The iNCAadditionally enables employees such as collection agents to improveefficiency of the collections processes by not only generating automaticpriorities but also via automated email indexing, sending outacknowledgements, gathering data to enable the collection agent to makea call to the customer and to allow the collection agent to easily enterrelevant post-call information after concluding the call. After thecall, the iNCA receives the data obtained by the collection agent fromthe call and updates the current attributes data of the work itemassociated with that collections matter. The iNCA also includes areal-time dashboard that provides not only access to the variousfunction of the iNCA that pertain to the processing of the work items,but also provides performance statistics regarding the processing of thecollections matters by the iNCA and the employees participating in therobotics operations control system.

The work items pertaining to disputed bills are forwarded to the DRA.When a customer receives a bill for which the customer contends that oneor more attribute values are erroneous either via an email, a voice callor other communication modes, the matter is forwarded for processing tothe DRA. An employee may call the customer to confirm that theinformation in the communication is correct and that the customerintends to raise a dispute. The employee may register a dispute afterthe call and the current attributes data of the billing matter isupdated to indicate that it is under dispute. More particularly, thecurrent attributes data may include the precise attributes that thecustomer contends are in error, identifying indicia for the dispute suchas the bill or invoice number, a dispute registration number if any, thedate the dispute was raised, the current status of the dispute and thelike. An AI assisted investigator is included in the DRA which gathersthe information necessary to check the accuracy of the customer's claimsand presents the information to an employee. Based on the informationobtained from the AI assisted investigator, an employee who is assignedthe work item may determine that the dispute is valid, invalid orrequires further information. Accordingly, if the dispute is valid, theemployee may initiate a process to credit the customer. If the disputeis invalid or requires further query, the employee may respectivelyreject the dispute or transmit a query for further information. Forvarious communications that employees may need to send to customers, therobotics operations control system may provide communication templatessuch as pre-made email templates that the employee may quickly customizeto suit a situation with values from the current attributes data. A cashprocessor is also included within the robotics operation control systemto receive and send payments, to match received payments to one or morepending bills, track unidentified payments and the like.

The robotics operations control system as disclosed herein uses robotassistants such as the IBA, the iNCA and the DRA that mimic humanactions in executing complex tasks that are typically performedmanually. The various elements such as the workflow managers, disputesprediction model, automated invoice sender, AI assisted investigator andthe like lower operational costs through automatic processing of billingand collections tasks. For example, the workflow managers and AIassisted billing mailbox managers analyze received emails to not onlyupdate the current attributes data but also handle certain tasks such asinvoice requests or requests for account statements automaticallythereby reducing routine tasks for human employees. Elements such as thedispute prediction model enable automatic generation of checklists thatare customized to a given bill thereby greatly reducing the likelihoodof occurrence of dispute. Consequently, the time and resources thatwould otherwise have been expended on the dispute resolution are saved.The AI assisted investigator extracts the relevant documents and otherdetails for a human employee examining a dispute thereby saving the timeand effort that the employee would have otherwise spent in collectingthe required information. Also, the robotics operations control systemas described herein can be easily replicated across various geographiclocales thereby enhancing the efficiency of a global organization.

FIG. 1 shows a robotic operations control system 100 that enables ablended workforce including human employees and robots includingprocessors to cooperatively carry out tasks associated with retrievinginformation, analyzing the retrieved information and providingactionable items for the human employees to execute. The robotstherefore function as assistants to the employees and aid in variedtasks across the robotic operations control system 100 such as but notlimited to, assignment of work items, monitoring or tracking theprogress on the processing of the work items, determining disputeprobabilities on billing matters or researching dispute data andmaintaining performance statistics. The robotic operations controlsystem 100 includes robots or robot assistants such as an intelligentbilling assistant (IBA) 120, an intelligent collections assistant (iNCA)130, a disputes resolution assistant (DRA) 140 and a cash processor 160.Each of the robot assistants include one or more programmed processorswhich are configured to contextually provide the requisite data to theemployees who use the robotic operations control system 100 to handlethe various billing or collections matters. The robot assistants arealso configured to execute some tasks automatically without manualintervention. The robotic operations control system 100 thereforeenables seamless interactions between robot assistants and the employeesto execute the various tasks smoothly. In addition to facilitation task,the robotic operations control system 100 tracks the performance of theblended workforce and maintains the performance statistics 156 which canbe viewed via the respective real-time dashboards 132, 146, 184 and 168included in each of the IBA 120, the iNCA 130, the DRA 140 and the cashprocessor 160.

While the IBA 120, the iNCA 130, the DRA 140 and a cash processor 160may each have their own databases, the robotic operations control system100 may include a central repository 150 for data that is required foroperation of the sub-systems. The central repository 150 may storecurrent attributes data 152 and historic attributes data 154 of the workitems. The current attributes data 152 may include attribute data ofcurrently pending bills or collection matters. Historic attributes data154 may include attributes of previous bills or collection matters thathave been resolved or closed. Moreover each of the robot assistants, theIBA 120, the iNCA 130, the DRA 140 and the cash processor 160 mayinclude a respective workflow manager that automatically prioritizesitems on work lists so that the employees are aware of the order inwhich the work items are to be dealt with and UIs such as real-timedashboards that convey, in addition to other information detailed infra,performance statistics of respective robot assistants.

The IBA 120 handles various matters to be billed in addition to theworks in progress with regards to billing and identifying billingmatters that have a potential for dispute based, for example, on pasthistory. The IBA 120 includes a workflow manager 122, a disputesprediction model 124, an AI assisted billing mailbox manager 126, amaster data manager 128 and a real-time dashboard 132. The workflowmanager 122 accesses work items obtained from an electronic billingmailbox 1222 and prioritizes the work items based not only on thecurrent attributes data 152 of the work items but also the historicattributes data 154. By the way of illustration and not limitation, thework items may include new unread emails within the billing mailbox 1222and also billing matters which may be work-in-progress (WIPs). Examplesof the current attributes data 152 may include without limitation, acustomer associated with the work item/email, document identifiers suchas bill or invoice numbers, the associated invoice amounts, the invoicedates, characteristics peculiar to a matter for which the invoice isgenerated and the like. The characteristics that are peculiar to theinvoice matter depend on the goods or services for which the invoice isgenerated. For example, if the invoice is generated for a deliveredpackage, the characteristics may include the weight of the package, thedistance it was transported, taxes or duties that were paid on thepackage and the like. Similarly, various goods and services may havetheir own characteristics that form part of the current attributes data152. In addition, the current attributes data 152 of a billing mattermay depend on the status of the billing matter. As the billing matterprogresses towards invoice generation, collections and cash processing,the current attributes data 152 may expand to also include informationregarding how the billing matter was resolved. For example, if the billor invoice was disputed and had to be processed by the DRA 140, thendetails regarding the dispute such as the characteristics that weredisputed and how the dispute was resolved may form part of the currentattributes data 152. In an example, the priorities may be set daily notonly for an entire billing department but also for each employee whowill be assigned the work items. The workflow manager 122 accessespriorities of the work items and match them with the characteristics ofmembers of the blended workforce including robotic assistants or humanemployees. For example, the members identified within the roboticoperations control system 100 may be included in a team matrix 158 whichincludes information regarding the robotic assistants and the employees.Employee information such as but not limited to, employee name, employeeidentification, location, overall experience, tenure in the currentposition, time period that the employee was registered with the roboticoperations control system 100, education and skills of the employees,language proficiency, record of successful outcomes and the like can beincluded in the team matrix 158. Information regarding the roboticassistants such as their functions, outcome statistics and the like mayalso be included in the team matrix 158.

Based on the information from the team matrix 158, the workflow manager122 may automatically assign work items to different employees.Generally, the work items may be assigned serially to differentemployees. However, in some cases where the work items are more complex,they may be matched up with employees having higher education and/orskills or who have greater experience. In some cases a work item mayrequire specialized skills such as proficiency in a particular languageas indicated by the current attributes data 152 of the work item. Theworkflow manager 122 may automatically assign the work item to anemployee who is registered within the team matrix 158 as proficient inthat language. The employee priorities may be assigned by automaticallyarranging work items assigned to the employee in a descending order oftheir departmental priority numbers. The work items that are thusprioritized may be presented to a supervisor in order that they may beassigned to individual employees.

The disputes prediction model 124 can be a statistical model thatreceives as inputs the current attributes data 152 and historicattributes data 154, analyzes the inputs to estimate the likelihood ofdispute in each newly created billing matter. In an example, thelikelihood of dispute for each attribute of the bill in the currentattributes data 152 is obtained. Based on a comparison of thelikelihoods of disputes with predetermined thresholds, a checklist maybe automatically generated for the bill. The checklist may includecheckpoints or confirmations for values of the attributes of the bill.During the generation process, the bill is run through the checklist forverification of the attributes prior to sending it out to the payer. Thedisputes prediction model 124 enables proactive action in reducing theoccurrence of a dispute thereby saving time and cost.

The AI assisted billing mailbox manager 126 enables increasing theefficiency of the billing process by effectively tracking the completionof requests. When an email which is initially received in the billingmailbox 1222, it is automatically read by the AI assisted billingmailbox manager 126 using, for example, natural language processing(NLP). A ticket is logged and an acknowledgement may also beautomatically sent to the requestor. The AI assisted billing mailboxmanager 126 obtains other information from processing the email via theNLP techniques to provide for example, the current attributes data 152including identifying indicia such as an bill number for a billingmatter referred to in the email. Based on the current attributes data152 such as, the type of request, various actions may be initiated bythe IBA 120 as will be detailed further infra. The master data manager128 manages the data generated from the various billing matters such asbut not limited to the current attributes data 152 and historicattributes data 154, performance statistics 156 and other dataassociated with the robotic operations control system 100. The datamanaged by the master data manager 128 can be viewed by UIs such as thereal-time dashboard 132 which lets an employee explore variousattributes of a billing matter based of course on the privilegescorresponding to the employee's profile with the robotic operationscontrol system 100.

Then iNCA 130 and the DRA 140 may share an electronic collectionsmailbox 1340 in accordance with an example. This is because, customersmay send emails regarding payments, disputes and the like from externaldomains into the collection mailbox 1340. The iNCA 130 also includes aworkflow manager 134, an AI prioritizing assistant 136, a promise-to-pay(PTP) processor 138, a dispute registration manager 142, an automatedinvoice and statement sender 144 and the real-time dashboard 146.

The workflow manager 134 and the AI prioritizing assistant 136 work in amanner similar to the workflow manager 122 for prioritizing purposes.However, prior to the prioritizing of work items, the workflow manager134 initially separates emails to be processed by the iNCA 130 and theDRA 140. The workflow manager 134 accesses the emails in the collectionsmailbox 1340 to analyze if the email needs to be processed by the iNCA130 or the DRA 140. In an example, a collections matter may be escalatedas a dispute which is processed by the DRA 140. Based on the analysis,the workflow manager 134 included in the iNCA 130 may be able toidentify if the matter should be processed by the iNCA 130 or the DRA140. In an example, the robotic operations control system 100 may importthe emails daily from the email server to the collections mailbox 1340included within the robotic operations control system 100 so that theemails are only accessible from the collections mailbox 1340. The iNCA130 processes work items to enable pre-call activities such as providinginformation prior to a human collection agent calling a customer basedon the information provided by the iNCA 130. Upon completion of thecall, the iNCA 130 also processes the work items to receive informationexchanged during the call, such as PTPs, in post-call activities. Theinformation thus gathered may form part of the current attributes data152 which will be accessed for further processing.

Again, the AI prioritizing assistant 136 accesses the work items whichmay include the emails retrieved from the collections mailbox 1340 andanalyzes them to identify priorities for the work items or collectionsmatters for the day. An Accounts Receivables (AR) report may begenerated daily which includes the emails received in the collectionsmailbox 1340 and any pending collections matters that have been flaggedfor action on a given day. As mentioned above with respect to billingmatters, collections matters may also have department-level prioritieswhich are indicated to assigned employees. The employees in turn mayaccess the work items they have been assigned in a descending order oftheir department-level priorities. In an example, a supervisor may beable to manually configure or adjust the priorities if needed. A PTPprocessor 138 can be used to register promises to pay made by partieswho have outstand bills to be paid. The PTP processor 138 functions intandem with the cash processor 160 to track payments in accordance withexamples discussed herein.

The iNCA 130 may be configured to automate certain tasks necessitated bythe work items in an AR report. The automated statement and invoicesender 144 may be configured to access a data store such as the centralrepository 150 to retrieve the bills. If an invoice is available for agiven work item, the automated invoice sender 144 selects the invoicefor transmission to the requester. The invoice may be selected based onone or more matching data fields such as but not limited to atransaction number, contact information and the like from the currentattributes data 152 of the work item. Similarly, the automated statementand invoice sender 144 may also send out statements for customeraccounts in response to requests in the collections mailbox 1340.

There can be a subset of emails in the collections mailbox 1340disputing invoices that were previously sent. The workflow manager 134in the iNCA identifies those emails as pertaining to disputes. Thedispute emails can be recognized by parsing the email text and using NLPtechniques to match the parsed email text with the current attributesdata 152. The current attributes data 152 of the work item may contain atransaction number, data such as an invoice number, a case numberrelated to the dispute and the like. The workflow manager 134 may beconfigured to search for particular data fields or textual patterns suchas words, phrases and the like within the emails pertaining to disputes.If the email refers to an existing dispute, the workflow manager 134 maybe configured to automatically forward the email for processing to theDRA 140. If the email does not refer to an existing dispute or if noother dispute is registered for the given invoice number or transactionnumber but the email includes words indicative of dispute, then thedispute registration manager 142 is activated to register a dispute forthe given invoice number. In an example, the dispute registrationmanager 142 may raise a ticket requesting human intervention. This canresult in an employee calling the corresponding contact in response tothe email and initiating a dispute process.

A workflow manager 172 included in the DRA 140 functions similarly tothe workflow manager 122 included in the IBA 120 and the workflowmanager 134 included in the iNCA 130 to identify emails or other workitems that are to be processed by the DRA 140. An AI assisted manager174 enables prioritizing the dispute tasks by the DRA 140. As mentionedsupra, the priority ranks of the work items in the DRA that aregenerated each day can be used by an employee assigned to the work itemsto prioritize his/her work queue. The AI assisted investigator 176provides documentation relevant to a work item being processed by theDRA 140. In an example, the AI assisted investigator 176 is configuredto obtain documents or items from the central repository 150 thatenables an employee assigned to the work item to further investigate thedispute and determine the validity of the dispute. For example, if adispute is logged for the invoiced amount of a bill, the AI assistedinvestigator 176 may be configured to obtain documents or emails relatedto the disputed issue. In addition, the AI assisted investigator 176 maybe configured to identify a data field under dispute and obtain valuesof the data field from different documents thereby enabling the employeeto determine the validity of the dispute. If the dispute is determinedto be valid, the employee may access the invoice sender 178 to send arevised invoice if the bill is not yet settled. If the billed party hasalready paid the bill and the dispute is found to be valid, the employeemay use the credit processor 182 to process the credit to the billedparty. If the dispute is determined to be invalid, the employee mayautomatically be provided with a pre-made template email to send arejection to the contact associated with the bill. The language in thepre-made template email may be framed based at least on the reason codesof the dispute in one example. In an example, the employee may beprovided with a plurality of possible dispute scenarios by the DRA 140.When the employee selects a dispute scenario, the corresponding emailtemplate conveying the rejection may be provided to the employee.

The cash processor 160 which also forms a part of the robotic operationscontrol system 100 is used to process the debits and credits associatedwith the various work items. The work items for the cash processor 160may not only include documents but may also include cash, checks,credits, debits and the like which are to be processed. When a paymentfor a bill or invoice is to be deposited or if any credits are received,it can be sent to the cash processor 160. In an example the cashprocessor 160 may also include a workflow manager 162 that prioritizesitems for the day for employees working with the cash processor 160.

A payments matcher 164 is also included to match payments to existingbills/invoices to identify which of the bills/invoices are to be markedas paid. When the deadline associated with a PTP expires, the PTPprocessor 138 may activate the payments matcher 164 from the cashprocessor 160 to verify if a payment corresponding to the PTP isobtained. If the payments matcher 164 determines that the bill is paid,then the billing matter associated with the work item is closed. If nopayment was identified for the billing matter, then the payments matcher164 may flag the PTP as withdrawn. The billing matter may then beprocessed by the workflow manager 134 of the iNCA 130 in accordance withexamples discussed herein. An unidentified payment tracker 166 whichalso forms a part of the cash processor 160 can be used to track andidentify a party/invoice that a payment is directed to. The tracking mayoccur based on an account number, invoice number, client id and thelike.

FIG. 2 is a block diagram of the AI assisted billing mailbox manager 126in accordance with one example. The AI assisted billing mailbox manager126 analyzes emails from the billing mailbox 1222 to identify a type ofrequest included in the email and generate an appropriate response inaccordance with examples discussed herein. The AI assisted billingmailbox manager 126 accesses an email, logs a ticket and automaticallysends an acknowledgement 214 to the requestor by the acknowledgementsender 208. A text parser 202 included in the AI assisted billingmailbox manager 126 parses the email text to generate word tokens. Theword tokens may be further processed to remove spaces, stop words,punctuation and the like. A data identifier 204 also included in the AIassisted billing mailbox manager, identifies data that should beincluded or updated in the current attributes data 152. For example, ifthe email refers to a customer id and invoice number, then the currentattributes data 152 may be updated to include a reference to the email.Moreover, metadata regarding the email such as the date and time ofreceipt may also be included in the current attributes data 152 by thedata identifier 204 under the combination of the customer id and thebilling number using text matching techniques for example. Subsequently,whenever the current attributes data 152 of the work item is accessed,information regarding the email may also be included within the currentattributes data 152.

Various types of billing requests can be processed by the AI assistedbilling mailbox manager 126. The various types of billing requests maypertain to updating the various attributes of a bill. If the type ofbilling request pertains to a change in options for one or moreattributes, the options to be added or deleted can be automaticallydetermined from the parsed text of the email obtained from the textparser 202. More particularly, the spreadsheet can be filtered based ontypes of options for each of the attributes, and the options can beadded or deleted as per the information obtained by parsing the emailincluding the billing request. If the type of billing request pertainsto a change in a product associated with the bill, then the dataidentifier may obtain a product identity from the parsed text, obtain aproduct code from product information that may be stored on the centralrepository 150 as among the various options available to the productattribute of the bill and the product code is automatically updatedwithin the current attributes data 152 of the bill. Similarly, if thetype of billing request pertains to a change in a rate or amount to bebilled, the rate to be updated is automatically determined from theparsed text, the data identifier can be configured to confirm that acurrent rate is different from a new rate to be updated and update thenew rate within the current attributes data 152 of the bill.

FIG. 3 is a block diagram of the workflow manager 134 in accordance withan example disclosed herein. Each of the IBA 120, the iNCA 130, the DRA140 include a respective workflow manager that accesses work items andgenerates daily priorities within a department and for individualemployees of the department. While the workflow manager 134 for the iNCA130 is discussed below, the details of the workflow manager 134 areequally applicable for the common functions executed by the workflowmanagers of the IBA 120 and the DRA 140. The details of such commonfunctions will be discussed below where relevant. The workflow manager134 includes a workflow analyzer 302, a pending matters report generator304, a work priority assistant 306 and a work item allocator 308.

The workflow analyzer 302 included in the workflow manager 134 accessesand analyzes the emails from the collections mailbox 1340 to identifywhich of the emails are to be processed by the iNCA 130 and the emailsthat are to be processed by the DRA 140. In an example, the emails fromexternal domains such as from the clients are received at thecollections mailbox 1340 while internal emails from the domain hostingthe robotic operations control system 100 are received at the billingmailbox 1222. The workflow analyzer 302 includes a text parser 3022, adata identifier 3024, an email sorter 3026 and a work item tracker 3028.

The text parser 3022 parses the text of each email accessed from thecollections mailbox 1340. The data identifier 3024 filters the stopwords, spaces, punctuation and the like from the parsed text and obtainsrelevant data from the email for updating the current attributes data152 related to the work item such as a collections matter. Similarly,information regarding the receipt of the email and the related metadatasuch as the date, time of receipt, the sender, and the like can also berecorded as part of the current attributes data 152. The data andmetadata can be updated to the current attributes data 152 using textmatching or pattern identification techniques and the like. Similartechniques may be employed by the email sorter 3026 to sort the emailsso that they are processed by the iNCA 130 or the DRA 140.

In an example, pattern matching techniques to identify a dispute casenumber or a bill number and the like may be used to allocate an email tothe DRA 140. For example, the current attributes data 152 may alreadycontain information to identify that a bill having a specific billnumber is under dispute with a dispute case number allocated to it.Other techniques such as tracking a status of the bill via the currentattributes data 152 may be employed by the email sorter 3026. Thus, evenif the email does not include a dispute case number, it may beautomatically forwarded to the DRA 140 for processing based on thebilling number included in the email. Multiple analysis techniques maytherefore be employed to identify and sort emails for being processedappropriately within the robotic operations control system 100. A workitem tracker 3028 keeps track of work items that were pending from theprevious days which are flagged to be completed on a given day. Forexample, a customer contact may not be available and may request a callback on the given day. In this case, the work item tracker 3028 followsup on such matters where the action is postponed to another day. Thepending matters report generator 304 receives the emails to be processedby the iNCA 130 in addition to the matters flagged for follow up by thework item tracker 3028 and generates a report of the matters that arepending for the day.

The pending matters report is then accessed by the work priorityassistant 306 that analyzes the pending matters and allocates prioritiesfor a period, such as, a day. In an example, the work items that areflagged for follow up on a given day such as during resubmission, mayhave higher priority. Other information that is considered for prioritycan include the historic attributes data 154 such as customer profileswherein customers with high volumes are given precedence over othercustomers with lower volumes. Furthermore, priorities may vary based onthe function such as billing, collections or disputes. In an example,intelligent collection analytics can lead to optimized collectionstrategy by classifying existing customers into groups which are thenassigned different priorities. For example, customers with overduebalances of over 80% can be classified as priority 1 while customerswith bills unpaid for more than 60 days can be priority 2 and the like.The prioritized work items are then assigned or allocated to the variousemployees either automatically by the work item allocator 308 or by asupervisory employee. Automatic assignment of work items can beconfigured based on the current attribute data 152 of the work items andcharacteristics of the workers as detailed in the team matrix 158. Workitems that are automatically assigned may be reviewed and altered by asupervisory employee. The work item assignments are received by the workitem allocator 308 which stores the assignment information and sendsemails to the respective employees regarding their work items for theday.

The workflow manager 122 of the IBA 120, the workflow manager of the DRA140 and the workflow manager 162 of the cash processor 160 also operatesimilarly in identifying priorities, receiving assignments andgenerating pending work item reports.

FIG. 4 shows a block diagram of the work priority assistant 306 inaccordance with an example. The work priority assistant 306 accessescurrent information of work items, analyzes the corresponding historicdata and produces priority for each of the work items. In an example,the priority of a work item can be selected from predefined prioritylevels associated with different groups of work items. An examplepriority scheme may involve a plurality of priority levels, for examplethree priority levels. In the case of billing matters, priority level 1may include high-value customers who have high accounts receivablesvalues associated therewith. Priority level 2 may involve aged workitems that are pending for more than a certain threshold time period. Ifa bill has been pending for more than a certain predefined timethreshold, the work item associated with the bill can be automaticallyassigned priority 2 by default. Priority level 3 may include those workitems which are customized for a geographic location or domain such as aspecific country as the robotic operations control system 100 can beimplemented across various countries. For example, work items related toa locally important customer may be assigned a priority level 3.

A current data retriever 402 retrieves the current attributes data 152related to a work item currently being processed by the work priorityassistant 306. The current data retriever 402 may obtain attribute dataof the work item such as a customer associated with the work item, theperiod of pendency associated with the work item, the geographiclocation or domain of the work item and the like. The historic dataanalyzer 404 analyzes the historic data ranging from, for example, thepreceding 16-18 months of the particular customer in order to identifythe customer's payment trend, the volume of payments from the customerand the like. Based on the inputs from the current data retriever 402and the historic data analyzer 404, the priority identifier 406 mayclassify the work item into one of the priority levels. If the work itemcan be classified into multiple priority levels, a higher priority levelmay be assigned to the work item. The priority levels defined aboveallow processing of high-value clients efficiently while simultaneouslyaddressing aged items or regionally important clients. While one schemeof prioritization is discussed herein by the way of example, it can beappreciated that other priority schemes may also be implemented inaccordance with examples described herein.

FIG. 5 shows a block diagram of the disputes prediction model 124 inaccordance with an example. The disputes prediction model 124 receivesinput data from the central repository 150 employs disputes predictionanalytics to estimate the likelihood of dispute in each newly createdbilling matter. When a billing matter is created or a bill is generated,the attributes collector 502 gathers the current attributes data 152 ofthe bill. In addition, the historic attributes data 154 of the bill'sattributes may also be obtained. By the way of illustration and notlimitation, if the bill pertains to a courier service, the attributes ofthe bill such as the customer shipping the package, the shippingdistance, the starting and the delivery locations, weight of thepackage, date the package was mailed, shipping speed of the package, theitems shipped within the package, taxes and duties on the package andthe like are collected by the attributes collector 502 from the currentattributes data 152. For each attribute that is obtained by theattributes collector 502, the disputes probability calculator 504estimates the probability of occurrence of the dispute using thehistoric attributes data 154. For a given attribute, the historicattributes data 154 of one or more of dispute history of the customerassociated with the bill and the dispute history of the attributesacross all the customers can be considered in the dispute probabilitycalculations. The disputes probability analyzer 506 compares the disputeprobability of each attribute with predetermined threshold values todetermine if the attribute has a high likelihood of being disputed. Ifthe dispute probability of the attribute is higher than the thresholdvalue, then the attribute may be marked for including in a checklist bythe disputes probability analyzer 506. The checklist generator 508receives information regarding each attribute whose dispute probabilityis higher than the respective threshold. The checklist generator 508automatically generates the checklist by including respectivecheckpoints which may include one or more questions in the checklist.The questions can be directed towards confirming the values of theattributes.

FIG. 6 is a flowchart 600 that details a method of processing an emailby the AI assisted billing mailbox manager 126 in accordance with anexample. The method begins at 602 wherein an email is accessed from amailbox. The email may have been sent by a helpdesk agent regarding acustomer call or other issues. At 604 a ticket is created for the emailso that the actions taken in response to the email can be tracked fromthe receipt of the email to the resolution of the issues raised in theemail. In an example, the ticket can be uploaded so that the status ofthe ticket can be obtained, for example, via the real-time dashboard132. At 606, the email text is analyzed to determine the identificationindicia of the billing matter and a request type. In an example, therobotic operations control system 100 is programmed to handle varioustypes of requests which may require specific keywords, codes and thelike. The sender of the email may place the codes, keywords or otherissue-identifying indicia within the email. The various billing requesttypes may include requests for change in options such as for options tobe added or deleted, a request for a product change wherein a productcode in the bill is to be updated or a change in the discountpercentages which results in the change if the billed amount. At 608, anauto-reply is transmitted to the sender of the email. In an example, theauto-reply may include information such as the issue-identifying indiciafrom the email so as to initiate the processes are to be executed inresponse to the email.

The information necessary for processing the request in the email isobtained at 610. For example, if the email is regarding an existingbilling matter that has already been through some processes, the casehistory, including documents such as invoices, delivery receipts,communications that were exchanged such as email chains, chat logs,calls to customer service agents, automated messages and the like can beobtained at 610. The email is added to the list of billing matterswithin a spreadsheet for example, for processing by the roboticoperations control system 100 at 612. The actions that can be executedfor completing the request or resolving the issues raised in the emailare selected at 614 based on the information in the email.

In some examples, the actions may include robotic processes that do notrequire human intervention whereas in some examples, manual input may benecessary to complete the actions. For example, if the email is arequest for bills or copies thereof, the actions selected at 614 caninclude automatically transmitting the bills to the correspondingcontacts included in the current attributes data 152 of the billingmatter via a robotic process without the need for manual input. At 616,it is determined if the request is completely addressed. If yes, themethod proceeds to send confirmation email at 618 regarding completionof the request to the supervisor and/or the billing mailbox 1222 and themethod terminates on the end block. If it is determined at 616 that therequest is not complete or the issues in the email could not becompletely addressed via automatic actions selected at 614, the email isflagged for manual input or for assignment to an employee at 620 and themethod terminates on the end block. During manual processing, anemployee who is assigned the work item may request access to theinformation corresponding to the work item and the email, along withother documents and the current attributes data 152 of the work item isprovided to the employee.

FIG. 7 is a flowchart 700 wherein a disputes prediction model 124 isemployed to generate a checklist that enables reducing disputes inaccordance with an example. The method begins at 702 wherein a billingmatter is selected for processing and the current attributes data 152for the billing matter is obtained at 704. The corresponding historicattributes data 154 is retrieved at 706. The values within the currentattributes data 152 are analyzed in view of the historic attributes data154. For example, the current attributes of the billing matter mayinclude a customer name, a billed amount, a matter for which the amountwas billed, a billing period and the like. The historic attributes data154 can include prior values of the various attributes of the bill for agiven customer such as prior billed amounts, prior billing periods,matters which were previously billed for and the like.

For each of the current attributes, a probability of occurrence ofdispute within each of the current attributes based on the historicattributes data 154 is obtained at 708. Thus, for a customer name or acustomer id, the probability of occurrence of dispute in the currentbilling matter is calculated based on prior transaction history of thecustomer for similar billing matters. Likewise, probabilities ofoccurrences of disputes for a billed amount, a billing period and thelike can be calculated based on the prior transaction history stored asthe historic attributes data 154. The probabilities of occurrences ofdisputes for the current attributes of the billing matter are comparedwith respective predetermined thresholds at 710. The predeterminedthresholds may be calculated as the number of billing matters disputedwithin the total billing volume for the attribute in accordance with oneexample. Other measures may also be employed for determining thethresholds in accordance with examples described herein. If theprobability of occurrence of the dispute is greater than the respectivethreshold for a given attribute, then a respective check point for theattribute such as a question is added to the checklist generated at 712,else the method terminates on the end block. A checklist customized to aparticular bill is thus generated automatically based on the calculatedprobabilities of occurrences of disputes in each of the attributes of abilling matter which enables proactively working to avoid disputes andthe resultant wastage of time and resources in dispute resolutions.

FIGS. 8A and 8B are flow diagrams 800 and 850 respectively thatillustrate how a blended workforce manages an example task such as adispute resolution via seamlessly switching between the manualoperations 840 conducted by human employees, RPA 820 and AI 830 withinthe robotic operations control system 100. The RPA 820 and the AI 830can be incorporated into one or more of the parts of the DRA 140 asdescribed supra. An RPA process may be carried out automatically withcertain pre-programmed instructions via definite process steps. An AIprocess can be carried out automatically via pre-programmed instructionsthat include processing/analysis of data which may requires the AIelements to make decisions or provide suggestions. Thus, the process ofanalyzing the dispute details and suggesting actions 822 are AIprocesses executed by the AI elements.

When a dispute email is received 802 at the source 860 such as thebilling mailbox 1222, the email is automatically extracted and updated804 to the DRA 140 for resolution and flagged 806 for further manualinput from an employee. The email is then processed under manualoperations 840 wherein a supervisor may allocate the email to anemployee who views the email 808, makes a call 810 to the correspondingcustomer and enters the details 812 in the DRA 140. For example, theemployee may call the contact or the concerned party at the customer wholaunched the dispute to verify the attributes of the billing andcollection matters if any. In an example, the employee may confirm thatthe dispute exists and the reason for the dispute with the customer andenter appropriate reasons codes that may be configured within the DRA140. The details which are manually verified may be entered 812 withinthe DRA 140.

The details that are thus collected along with other details that mayexist within one or more of the current attributes data 152 and thehistoric attributes data 154 are automatically fetched 814 along withthe corresponding documents 816. For example, the bills, emailexchanges, recorded voice conversations, chat and instant messaging logsand the like associated with the billings and/or collections matter arefetched at 814 and 816. The details are analyzed at 818 and thesuggested actions 822 are retrieved based on the analysis. For example,the analysis may be based on the reason codes, and the facts associatedwith the reason codes can be verified from the documents and if thefacts bear out, the actions to resolve or remedy an error that causedthe dispute may be automatically suggested by the AI 830 of the DRA 140.The procedure again awaits manual selection 824 from the automaticallysuggested actions. Based on the verification of the facts and thesuggested actions, the employee further decides 826 if a dispute shouldbe raised. If the employee decides that a dispute is to be raised, thedispute is registered 828 for further processing else the email may beforwarded for further query or the dispute is rejected 832 by theemployee if the facts do not conform or are inconsistent.

FIG. 8B shows the backline processing that occurs seamlessly between theRPA 820 and the manual operations 840 when a dispute is registered inaccordance with the examples described herein. When a dispute isregistered 828, the RPA 820 automatically retrieves the dispute 854 andfetches the case details 856, for example, when an access request isreceived from an employee who is assigned the work item associated withthe dispute. The case details at 856 would include the informationentered into the DRA 140 during the call made at 810 which may includedata such as the reason codes. During the manual operations 840, thecase details 858 are viewed by an employee who is assigned to the caseand the employee determines if the dispute is valid and should beaccepted 862. If the dispute is accepted, an approval email isautomatically created 864 by the RPA 820 and sent 868 to the concernedparties. The information regarding the approval is also automaticallyuploaded to the DRA 140. In case the employee decides that the disputeis not valid or has further questions regarding the dispute, theemployee selects appropriate scenarios which fit the dispute conditionsand circumstances in addition to the attachments and the recipients tosend 884 one of a query or rejection. Selection of the appropriatescenario can provide a pre-made template email that already includeslanguage that fits the dispute conditions. The employee may quicklycustomize the email with the customer name, dispute registration numberand other attribute data prior to sending it out to the recipients. Theinformation regarding the rejection and query is also uploaded 882 tothe DRA 140. A real-time dashboard is generated 886 which provides thetimelines for dispute resolution in addition to the functions carriedout by the RPA 820, AI 830 and the manual operations 840. The real-timedashboard enables a supervisor to judge the efficiency of variouselements of robotic operations control system 100 and identifyopportunities for improvement.

FIG. 9 shows a user interface 900 which enables assignment of work itemsto employees in the DRA 140 in accordance with examples disclosedherein. A list of work items 902 is displayed to a supervisor. In anexample, the list of work items 902 may be ordered based on prioritiesgenerated automatically as described herein. The supervisor may selecteach work item and select an employee who will be assigned the work itemfrom a dropdown list box 906. Successful assignment of the work item isconveyed to the supervisor via output such as a pop up box 904. It canbe appreciated that similar assignment schemes are also implemented inthe IBA 120 and the iNCA 130 in accordance with examples describedherein.

FIG. 10 shows another user interface 1000 that displays the variousclient ids and billing matter categories which were significantlydisputed as determined from the historic attributes data 154 inaccordance with an example. The customers are identified by accountnumber 1002 and name 1004 while the categories 1006 may be identified bycodes as shown. The percentage of likelihood 1008 that a dispute arisesfor the client for the specific category is shown along with thehistorical percentages 1010. In an example, the percentage may beobtained based on the calculations by the disputes prediction model 124.When an employee selects to take action 1012, a checklist can begenerated as described herein.

FIG. 11 shows a checklist 1100 that is generated in accordance withexamples described herein. The checklist contains a number of questionsthat are automatically generated based on the probability of dispute foreach attribute of the billing matter. An employee is supposed to verifythe checklist0 prior to submitting a bill in order to reduce theprobability of dispute. When the employee submits a completed checklist,the data may be recorded within the central repository 150 for laterretrieval.

FIG. 12 shows a user interface (UI) or a real-time dashboard 146 of theiNCA 130 in accordance with an example. The real-time dashboard 146shows a summary of the workflow within the iNCA 130 at any given time.The number of work items for each of the three priorities that areassigned 1202, that were touched 1204 and the current pending work items1206 are shown. Other information such as the number of cases wherethere was customer contact 1208 for collections, the customers who werereached, who could not be reached and those which are re-submitted aredisplayed. When a customer was not reached or requested call back atanother specified time, the work item may be re-submitted 1212 foraction at that specified time. The priority of the work item may beadjusted accordingly by the AI prioritizing assistant 136. Also, thestatus of the various promise to pay (PTP) 1214 wherein customerspromise to pay a certain amount at a later date and whether such PTPswere kept or withdrawn are also shown. Similarly the ‘reached’ status1216 which indicates if the right person was reached and thecontacted/action status 1218 are also tracked.

FIG. 13 shows a resubmission screen 1300 in accordance with an example.The resubmission screen 1300 includes a Try Again Date 1302, a Try AgainTime 1304, the Reason 1306 for having to try again and the notes made bythe employee. In an example, the notes may be automatically supplied asa template within the iNCA 130. The resubmission will indicate to anemployee that the work item can be prioritized lower until the Try AgainDate occurs when the work item needs to be moved up the priority list.

FIG. 14 shows a screen 1400 to raise a dispute in accordance with anexample. The current attributes data 152 of the work item such as thetitle 1402, the invoice number 1406, reason for the dispute 1408, thereason code 1412 and the comments 1410 associated with the dispute whichmay have been entered manually or automatically.

FIG. 15 shows a dashboard of the DRA 140 that in accordance with anexample. The screen also includes links to the dashboard 140 itself, alink to the unassigned work items 1504, a link to the assigned workitems 1506 and related notifications. The dashboard 1500 includes tabs1512, 1514 and the like that show statistics for disputes raised viavarious modes of communication. For example, the tab 1512 shows thestatistics 1508 raised for incoming calls. The tab 1514 shows thestatistics for disputes raised via emails. The statistics 1508 mayinclude a count of the disputes which were registered, the disputeswhich were created, the disputes which were resolved and disputes whichwere queried over different time periods and the like.

FIG. 16 shows an email 1600 that was automatically generated for acommunication regarding a dispute in accordance with an example. Thesubject of the email 1602, the recipients in the “to” address line 1604and the body of the email 1606 may be been obtained from apre-configured template which is customized to include data from thecurrent attributes data 152 of the work item. In addition, certainattachments may be already included with the email. However, a browsebutton 1608 enables an employee to add files as needed. Also, each ofthe email fields, 1602, 1604 and 1606 may be further customized by theemployee.

FIG. 17 shows a dispute validation screen 1700 in accordance with anexample. When it is determined that a dispute is valid, not valid orfurther information is required after an investigation, the disputevalidation screen 1700 allows an employee to resolve the dispute viaarranging for a credit, rejecting the dispute or raising a queryrespectively. The dispute validation screen 1700 shows the fields for adispute which may include an amount 1702, an invoice value 1704, aconsignment number 1706, an original invoice value 1708 and thedifference amount 1710. In addition, the buttons for issuing a creditnote 1712, raising a query 1714 or rejecting the dispute 1716 are alsoincluded. If one of the credit note 1712, raise query 1714 or rejectdispute 1716 buttons are selected, the associated processes areautomatically set in motion via the RPA 820.

FIG. 18 illustrates a computer system 1800 that may be used to implementthe robotic operations control system 100. More particularly, computingmachines such as desktops, laptops, smartphones, tablets, wearableswhich may be used to generate or access the reports may have thestructure of the computer system 1800. The computer system 1800 mayinclude additional components not shown and that some of the componentsdescribed may be removed and/or modified.

The computer system 1800 includes processor(s) 1802, such as a centralprocessing unit, ASIC or other type of processing circuit, input/outputdevices 1812, such as a display, mouse keyboard, etc., a networkinterface 1804, such as a Local Area Network (LAN), a wireless 802.11xLAN, a 3G or 4G mobile WAN or a WiMax WAN, and a computer-readablemedium 1806. Each of these components may be operatively coupled to abus 1808. The computer-readable medium 1806 may be any suitable mediumwhich participates in providing instructions to the processor(s) 1802for execution. For example, the computer-readable medium 1806 may benon-transitory or non-volatile medium, such as a magnetic disk orsolid-state non-volatile memory or volatile medium such as RAM. Theinstructions or modules stored on the computer-readable medium 1806 mayinclude machine-readable instructions 1864 executed by the processor(s)1802 to perform the methods and functions of the robotic operationscontrol system 100.

The robotic operations control system 100 may be implemented as softwarestored on a non-transitory computer-readable medium and executed by oneor more processors. For example, the computer-readable medium 1806 maystore an operating system 1862, such as MAC OS, MS WINDOWS, UNIX, orLINUX, and code 1864 for the robotic operations control system 100. Theoperating system 1862 may be multi-user, multiprocessing, multitasking,multithreading, real-time and the like. For example, during runtime, theoperating system 1862 is running and the code for the robotic operationscontrol system 100 is executed by the processor(s) 1802.

The computer system 1800 may include a data storage 1810, which mayinclude non-volatile data storage. The data storage 1810 stores any dataused by the robotic operations control system 100. The data storage 1810may be used to store real-time data from the current attributes data152, historic attributes data 154, performance statistics of the roboticoperations control system 100 and the like.

The network interface 1804 connects the computer system 1800 to internalsystems for example, via a LAN. Also, the network interface 1804 mayconnect the computer system 1800 to the Internet. For example, thecomputer system 1800 may connect to web browsers and other externalapplications and systems via the network interface 1804.

What has been described and illustrated herein is an example along withsome of its variations. The terms, descriptions and figures used hereinare set forth by way of illustration only and are not meant aslimitations. Many variations are possible within the spirit and scope ofthe subject matter, which is intended to be defined by the followingclaims and their equivalents.

What is claimed is:
 1. A robotic operations control system, comprising:one or more processors; and a non-transitory data storage mediumcomprising instructions that cause the processors to: access at leastone list of work items that are to be assigned to a blended workforce,the blended workforce comprising one or more human employees and one ormore robot assistants that include at least one of the processors;receive assignments of work items to one or more of the employees;wherein if at least one of the work items is associated with a matter tobe billed, causing the one or more robot assistants to: analyze currentattributes data and historic attributes data of the work item using adispute prediction model; determine a likelihood of a dispute based onthe analysis; provide a checklist to an employee of the one or morehuman employees assigned to the work item, the checklist comprisingcurrent attributes of the matter to be confirmed by the employee inresponse to the likelihood being higher than a threshold value; whereinif at least one of the work items is associated with a billing request:retrieve emails associated with the work item; parse the emailsassociated with the work item; extract identifying indicia and a type ofbilling request for the work item via the parsing of the emails whereinthe type of billing request pertains to one of a change in options, aproduct change or a rate change; provide the emails along with theidentifying indicia for the work item in response to an access requestfrom an employee assigned to the work item; wherein if at least one ofthe work items is associated with a collection matter: set a priorityfor the work item relative to other work items associated withcollection matters, the priority being based at least on the currentattributes data and the historic attributes data of the work item;provide pre-call information including the current attributes data forthe work item that has been prioritized; receive and store post-callinformation to the current attributes data for the work item; wherein ifat least one of the work items is associated with a dispute: retrievecurrent attributes data including the post-call information and historicattributes data for the work item; receive an input from the employee onwhether or not a dispute is to be registered; if a dispute is to beregistered: register the dispute with at least one of the robotassistants; enable processing of the dispute by the one or more robotassistants; if a dispute is not registered: receive the employee'sselection of a scenario, recipients and attachments for one of a queryor rejection; and send a communication to a contact party associatedwith the dispute regarding the querying or the rejection of the dispute.2. The robotic operations control system of claim 1, wherein theinstructions to determine the likelihood of a dispute further compriseinstructions that cause the processors to: receive access requests forthe work items from respective employees to whom the work items areassigned.
 3. The robotic operations control system of claim 1, whereinthe instructions to determine the likelihood of a dispute furthercomprise instructions that cause the processors to: retrieve the currentattributes data and the historic attributes data of the work item,wherein the historic attributes data includes attributes of bills thatwere disputed.
 4. The robotic operations control system of claim 3,wherein the instructions to determine the likelihood of a disputefurther comprise instructions that cause the processors to: calculate aprobability of occurrence of dispute for each attribute in the currentattributes data based on occurrences of disputes for the attribute inthe historic attributes data; and compare the probability of occurrenceof dispute for each attribute with a respective predetermined threshold.5. The robotic operations control system of claim 4, wherein theinstructions to provide the checklist further comprise instructions thatcause the processor to: automatically add a checkpoint for the attributeto the checklist if the probability of occurrence of dispute for theattribute is higher than the predetermined threshold.
 6. The roboticoperations control system of claim 1, wherein the instructions to enablepre-call activities further comprise instructions to: retrieve emailsassociated with the work item; parse the emails associated with the workitem; and extract identifying indicia for the work item via the parsingof the emails.
 7. The robotic operations control system of claim 1,wherein the type of billing request pertains to a change in options andthe non-transitory data storage medium further comprises instructionsthat cause the processors to: automatically determine from the billingrequest, attributes of the work item to be added or deleted.
 8. Therobotic operations control system of claim 7, wherein the type ofbilling request pertains to the change in options and the non-transitorydata storage medium further comprises instructions that cause theprocessors to: extract the attributes from the billing request to aspreadsheet; filter the spreadsheet based on types of options for eachof the attributes; and add or delete options as per request in thebilling request.
 9. The robotic operations control system of claim 1,wherein the type of billing request pertains to a change in the productand the non-transitory data storage medium further comprisesinstructions that cause the processors to: automatically determine aproduct code to be updated based on the billing request; and update theproduct code in the current attributes data of the work item.
 10. Therobotic operations control system of claim 1, wherein the type ofbilling request pertains to a change in a rate, the non-transitory datastorage medium further comprises instructions that cause the processorsto: automatically determine the rate to be updated; confirm that acurrent rate is different from a new rate to be updated; and update thenew rate within the current attributes data.
 11. The robotic operationscontrol system of claim 1, wherein to enable processing of the dispute,the non-transitory data storage medium further comprises instructionsthat cause the processors to: obtain files and extract data associatedwith the work item from the files.
 12. A method of enabling roboticoperations comprising: receiving current attributes data of a bill to beprepared; accessing historic attributes data of the bill to be prepared,the historic attributes data including prior values from previous billsfor each current attribute included in the bill; determining alikelihood of occurrence of a dispute for each current attribute of thebill based on respective historic attributes data; identifying at leasta subset of current attributes of the bill that have respectivelikelihoods of occurrences of disputes higher than respectivethresholds; generating a checklist that includes respective checkpointsfor the subset of the current attributes; providing the checklist forverification of the bill to be prepared; accessing, from an electronicmailbox, a plurality of work items associated with bills to becollected; parsing and analyzing text from the plurality of work items;obtaining current attributes data for each of the work items based onidentify indicia of the work items extracted from the parsed text;automatically assigning priorities to the work items based on thecurrent attributes data, the priorities being selected from a pluralityof priority levels; obtaining information required for completing thework items based on the current attributes data; identifying work itemsfor collections matters and work items corresponding to disputes usingthe current attributes data of the work items; automatically completingwork items that are configured for execution by robotic processes;flagging work items that are not configured for execution by roboticprocesses for manual input; and processing the work items correspondingto disputes to determine validity, invalidity or for query.
 13. Themethod of claim 12, further comprising: collecting data regarding workitems automatically completed by the robotic processes and the workitems that were completed on receiving the manual input.
 14. The methodof claim 13, further comprising: causing, via a real-time dashboard, adisplay of the data regarding the work items that are automaticallycompleted and the work items that are completed on receiving the manualinput.
 15. The method of claim 12, wherein identifying work itemscorresponding to disputes further comprises: identifying work itemscorresponding to disputes via tracking respective statuses of the workitems from the current attributes data.
 16. The method of claim 12,wherein processing the work items for dispute matters further comprises:obtaining a reason code for the dispute from the work item; andidentifying a reason for the dispute from the reason code.
 17. Themethod of claim 12, wherein processing the work items for disputematters further comprises: automatically initiating credits for the workitems that are determined to be valid disputes; and transmittingrespective rejection emails for the work items determined to be invaliddisputes.
 18. A non-transitory computer-readable storage mediumcomprising machine-readable instructions that cause a processor to:access at least one list of work items that are to be assigned to ablended workforce, the blended workforce comprising one or more humanemployees and one or more robot assistants that include at least one ofthe processors; receive assignments of work items to one or more of theemployees; wherein if at least one of the work items is associated witha matter to be billed, causing the one or more robot assistants to:analyze current attributes data and historic attributes data of the workitem using a dispute prediction model; determine a likelihood of adispute based on the analysis; provide a checklist to an employee of theone or more human employees assigned to the work item, the checklistcomprising current attributes of the matter to be confirmed by theemployee in response to the likelihood being higher than a thresholdvalue; wherein if at least one of the work items is associated with abilling request: retrieve emails associated with the work item; parsethe emails associated with the work item; extract identifying indiciaand a type of billing request for the work item via the parsing of theemails wherein the type of billing request pertains to one of a changein options, a product change or a rate change; provide the emails alongwith the identifying indicia for the work item in response to an accessrequest from an employee assigned to the work item; wherein if at leastone of the work items is associated with a collection matter: set apriority for the work item relative to other work items associated withcollection matters, the priority being based at least on the currentattributes data and the historic attributes data of the work item;provide pre-call information including the current attributes data forthe work item that has been prioritized; receive and store post-callinformation to the current attributes data for the work item; wherein ifat least one of the work items is associated with a dispute: retrievecurrent attributes data including the post-call information and historicattributes data for the work item; receive an input from the employee onwhether or not a dispute is to be registered; if a dispute is to beregistered: register the dispute with at least one of the robotassistants; enable processing of the dispute by the one or more robotassistants; if a dispute is not registered: receive the employee'sselection of a scenario, recipients and attachments for one of a queryor rejection; and send a communication to a contact party associatedwith the dispute regarding the querying or the rejection of the dispute.19. The non-transitory computer-readable storage medium of claim 18,further comprising machine-readable instructions that cause a processorto: automatically assign at least a subset of the work items to one ormore members of the blended workforce based on a match between thecurrent attribute data of the work items and data of the members asprovided in a team matrix.
 20. The non-transitory computer-readablestorage medium of claim 18, further comprising machine-readableinstructions that cause a processor to: provide pre-made template emailsthat including language suitable to conditions of the dispute inresponse to a selection the scenario.